Apparatus and method for demand response dispatch employing weather induced facility consumption characterizations

ABSTRACT

A method for dispatching buildings, including: generating data sets, each having energy values along with corresponding time and outside temperature values, where the energy values are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values; performing a machine learning model analysis on the each of the data sets; determining a least valued residual that indicates a corresponding energy lag for each of the buildings, the corresponding energy lag describes a transient energy consumption period preceding a change in outside temperature; using outside temperatures, model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict a dispatch order reception time for the demand response program event; and employing the dispatch order reception time to prepare actions required to control the each of the buildings to optimally shed energy specified in a dispatch order.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/984,785 (Docket: ENER.0133-C1), filed on Dec. 30, 2015, having acommon assignee and common inventors, and which is herein incorporatedby reference in its entirety. U.S. patent application Ser. No.14/984,785 is a continuation of U.S. patent application Ser. No.14/674,033 (Docket: ENER.0133), filed on Mar. 31, 2015.

This application is related to the following co-pending U.S. patentapplications, each of which has a common assignee and common inventors.

SERIAL NUMBER FILING DATE TITLE 14/984,612 Dec. 30, 2015 WEATHER INDUCEDFACILITY ENERGY (ENER.0131-C1) CONSUMPTION CHARACTERIZATION MECHANISM16/055,206 Aug. 6, 2018 MECHANISM FOR WEATHER INDUCED FACILITY(ENER.0131-C2) ENERGY CONSUMPTION CHARACTERIZATION 16/055,210 Aug. 6,2018 SYSTEM FOR WEATHER INDUCED FACILITY ENERGY (ENER.0131-C3)CONSUMPTION CHARACTERIZATION 16/055,212 Aug. 6, 2018 WEATHER INDUCEDFACILITY ENERGY (ENER.0131-C4) CONSUMPTION CHARACTERIZATION SYSTEM16/055,215 Aug. 6, 2018 APPARATUS AND METHOD FOR WEATHER (ENER.0131-C5)INDUCED FACILITY ENERGY CONSUMPTION CHARACTERIZATION 14/674,021 Mar. 31,2015 DEMAND RESPONSE DISPATCH SYSTEM (ENER.0132) EMPLOYING WEATHERINDUCED FACILITY ENERGY CONSUMPTION CHARACTERIZATIONS 14/984,706 Dec.30, 2015 APPARATUS AND METHOD FOR EMPLOYING (ENER.0132-C1) WEATHERINDUCED FACILITY ENERGY CONSUMPTION CHARACTERIZATIONS IN A DEMANDRESPONSE DISPATCH SYSTEM 15/903,533 Feb. 23, 2018 DISPATCH SYSTEMEMPLOYING WEATHER (ENER.0132-C2) INDUCED FACILITY ENERGY CONSUMPTIONCHARACTERIZATIONS 15/903,596 Feb. 23, 2018 SYSTEM FOR DEMAND RESPONSEDISPATCH (ENER.0132-C3) EMPLOYING WEATHER INDUCED FACILITY ENERGYCONSUMPTION CHARACTERIZATIONS 15/903,651 Feb. 23, 2018 WEATHER INDUCEDDEMAND RESPONSE (ENER.0132-C4) DISPATCH SYSTEM 15/903,705 Feb. 23, 2018DEMAND RESPONSE DISPATCH SYSTEM (ENER.0132-C5) EMPLOYING WEATHER INDUCEDENERGY CONSUMPTION 15/961,020 Apr. 24, 2018 DISPATCH PREDICTION SYSTEMEMPLOYING (ENER.0133-C2) WEATHER INDUCED FACILITY ENERGY CONSUMPTIONCHARACTERIZATIONS 15/961,073 Apr. 24, 2018 SYSTEM FOR DEMAND RESPONSEDISPATCH (ENER.0133-C3) PREDICTION EMPLOYING WEATHER INDUCED FACILITYENERGY CONSUMPTION CHARACTERIZATIONS (ENER.0133-C4)      WEATHER INDUCEDFACILITY ENERGY CONSUMPTION CHARACTERIZATION SYSTEM FOR DEMAND RESPONSEDISPATCH 14/674,041 Dec. 30, 2015 APPARATUS AND METHOD FOR PREDICTION OFAN (ENER.0134-C1) ENERGY BROWN OUT 14/674,057 Mar. 31, 2015 APPARATUSAND METHOD FOR DEMAND (ENER.0135) COORDINATION NETWORK CONTROL

BACKGROUND OF THE INVENTION Field of the Invention

This invention relates in general to the field of energy management, andmore particularly to a demand response prediction system that employsfine-grained energy consumption baseline data.

Description of the Related Art

One problem with resources such as electricity, water, fossil fuels, andtheir derivatives (e.g., natural gas) is related to supply and demand.That is, production of a resource often is not in synchronization withdemand for the resource. In addition, the delivery and transportinfrastructure for these resources is limited in that it cannotinstantaneously match production levels to provide for constantlyfluctuating consumption levels. As anyone who has participated in arolling blackout will concur, the times are more and more frequent whenresource consumers are forced to face the realities of limited resourceproduction.

Another problem with resources such as water and fossil fuels (which areubiquitously employed to produce electricity) is their limited supplyalong with the detrimental impacts (e.g., carbon emissions) of theiruse. Public and political pressure for conservation of resources isprevalent, and the effects of this pressure is experienced across thespectrum of resource providers, resource producers and managers, andresource consumers.

It is no surprise, then, that the electrical power generation anddistribution community has been taking proactive measures to protectlimited instantaneous supplies of electrical power by 1) imposing demandcharges on consumers in addition to their monthly usage charge and 2)providing incentives for conservation in the form of rebates and reducedcharges. In prior years, consumers merely paid for the total amount ofpower that they consumed over a billing period. Today, most energysuppliers are not only charging customers for the total amount ofelectricity they have consumed over the billing period, but they areadditionally charging for peak demand. Peak demand is the greatestamount of energy that a customer uses during a measured period of time,typically on the order of minutes. In addition, energy suppliers areproviding rebate and incentive programs that reward consumers for socalled energy efficiency upgrades (e.g., lighting and surroundingenvironment controlled by occupancy sensors, efficient cooling andrefrigeration, etc.) in their facilities that result in reductions ofboth peak and overall demand. Similar programs are prevalent in thewater production and consumption community as well.

Demand reduction and energy efficiency programs may be implemented andadministered directly by energy providers (i.e., the utilitiesthemselves) or they may be contracted out to third parties, so calledenergy services companies (ESCOs). ESCOs directly contract with energyconsumers and also contract with the energy providers to, say, reducethe demand of a certain resource in a certain area by a specifiedpercentage, where the reduction may be constrained to a certain periodof time (i.e., via a demand response program) or the reduction maypermanent (i.e., via an energy efficiency program).

The above examples are merely examples of the types of programs that areemployed in the art to reduce consumption and foster conservation oflimited resources. Regardless of the vehicle that is employed, what isimportant to both producers and consumers is that they be able tounderstand and appreciate the effects of demand reduction and efficiencyactions that are performed, say, on individual buildings. How does abuilding manager know that the capital outlay made to replace 400windows will result in savings that allow for return of capital withinthree years? How does an ESCO validate for a contracting regionaltransmission operator (e.g., Tennessee Valley Authority) that energyefficiency programs implemented on 1,000 consumers will result in a 15percent reduction in baseline power consumption?

The answers to the above questions are not straightforward, primarilybecause, as one skilled in the art will appreciate, weather drivesconsumption. Weather is not the only driver in consumption, but it issignificant. For instance, how can a building's energy consumption inJanuary of one year be compared to its consumption in January of anotheryear when average temperatures in the two months being compared differby 25 degrees? Is the difference between the two month's powerconsumption due to weather, or implementation of an energy efficiencyprogram, or a combination of both?

Fortunately, those in the art have developed complex, but widelyaccepted, normalization techniques that provide for weathernormalization of energy use data so that consumption by a building intwo different months can be compared without the confusion associatedwith how outside temperature affects energy use. These modelingtechniques provide for normalization of energy use data for buildingsand groups of buildings, and they are accurate for the above purposeswhen employed for energy use periods typically ranging from years downto days. That is, given sufficient historical energy use data (“baselinedata”), a model can be developed using these normalization techniquesthat can be used to accurately estimate the energy consumption of thebuilding as a function of outside temperature. These estimates are usedto remove weather effects from an energy use profile and also to predictenergy use as a function of temperature.

The present inventors have observed, however, that conventionalnormalization techniques, utterly fail to be accurate and useful whenenergy use data granularity is less than a 24-hour period. Normalizationmodels that are derived from energy use data having granularities on theorder of six hours, one hour, 15 minutes, etc., have been shown to beexceedingly deficient in accuracy and are thus unreliable.

Accordingly, what is needed is a technique that provides for accuratelyestimating energy use as a function of temperature, where the techniqueis derived from and is applicable to, energy consumption periods lessthan 24 hours.

What is also needed is an apparatus and method for employingfine-grained (i.e., less than 24 hours) energy use data to derive anaccurate model for energy use based upon outside temperature.

What is additionally needed is a fine-grained baseline energy dataweather normalization apparatus and method.

What is further needed is a system for characterizing a building'senergy consumption as a function of temperature that is applicable atresolutions less than one day.

What is moreover needed are mechanisms that understand and employ thetransient energy use responses of buildings for purposes of energyconsumption predictions covering individual buildings, groups ofbuildings, and larger areas.

SUMMARY OF THE INVENTION

The present invention, among other applications, is directed to solvingthe above noted problems and addresses other problems, disadvantages,and limitations of the prior art by providing a superior technique forpredicting a demand response event using an energy consumption baselinehaving a much finer granularity than that which has heretofore beenprovided. In one embodiment, a demand response dispatch predictionsystem is provided that includes baseline data stores, a building lagoptimizer, a dispatch prediction element, and a dispatch controlelement. The baseline data stores is configured to store a plurality ofbaseline energy use data sets for buildings participating in a demandresponse program. The building lag optimizer is configured to receiveidentifiers for the buildings, and is configured to retrieve theplurality of baseline energy use data sets from the baseline data storesfor the buildings, and configured to generate energy use data sets foreach of the buildings, each of the energy use data sets comprisingenergy consumption values along with corresponding time and outsidetemperature values, where the energy consumption values within the eachof the energy use data sets are shifted by one of a plurality of lagvalues relative to the corresponding time and outside temperaturevalues, and where each of the plurality of lag values is different fromother ones of the plurality of lag values, and configured to perform amachine learning model analysis on said each of the energy use data setsto yield corresponding machine learning model parameters and acorresponding residual, and configured to determine a least valuedresidual from all residuals yielded, the least valued residualindicating a corresponding energy lag for the each of said buildings,and machine learning model parameters that correspond to the leastvalued residual, and where the corresponding energy lag describes atransient energy consumption period preceding a change in outsidetemperature. The dispatch prediction element is coupled to the buildinglag optimizer and to weather stores, and is configured to receive, foreach of the buildings, outside temperatures, the corresponding energylag, and the corresponding non-linear model parameters, and isconfigured to estimate a cumulative energy consumption for the buildingsand is configured to predict a dispatch order reception time for ademand response program event. The dispatch control element is coupledto the dispatch prediction element is configured to receive the dispatchorder reception time and is configured to prepare actions required tocontrol the each of the buildings to optimally shed energy specified ina corresponding dispatch order.

One aspect of the present invention contemplates a system for predictinga dispatch for buildings participating in a demand response programevent. The system includes baseline data stores, a building lagoptimizer, a dispatch prediction element, and a dispatch controlelement. The baseline data stores is configured to store a plurality ofbaseline energy use data sets for the buildings. The building lagoptimizer is configured to determine an energy lag for one of thebuildings. The building lag optimizer has a thermal response processorand a machine learning model analysis engine. The thermal responseprocessor is configured to generate a plurality of energy use data setsfor the one of the buildings, each of the plurality of energy use datasets comprising energy consumption values along with corresponding timeand outside temperature values, where the energy consumption valueswithin the each of the plurality of energy use data sets are shifted byone of a plurality of lag values relative to the corresponding time andoutside temperature values, and where each of the plurality of lagvalues is different from other ones of the plurality of lag values. Themachine learning model analysis engine is coupled to the thermalresponse processor and is configured to receive the plurality of energyuse data sets and is configured to perform a machine learning modelanalysis on the each of the plurality of energy use data sets to yieldcorresponding machine learning model parameters and a correspondingresidual. The thermal response processor determines a least valuedresidual from all residuals yielded by the regression engine, the leastvalued residual indicating the energy lag for the one of the buildings,where the energy lag describes a transient energy consumption periodpreceding a change in outside temperature. The dispatch predictionelement is coupled to the building lag optimizer and to weather stores,and is configured to receive, for each of the buildings, outsidetemperatures, the corresponding energy lag, and the correspondingmachine learning model parameters, and is configured to estimate acumulative energy consumption for the buildings and is configured topredict a dispatch order reception time for the demand response programevent. The dispatch control element is coupled to the dispatchprediction element and is configured to receive the dispatch orderreception time and is configured to prepare actions required to controlthe each of the buildings to optimally shed energy specified in acorresponding dispatch order.

Another aspect of the present invention comprehends a method fordispatching buildings participating in a demand response program event,the method comprising: retrieving a plurality of baseline energy usedata sets for the buildings from a baseline data stores; generating aplurality of energy use data sets for each of the buildings, each of theplurality of energy use data sets comprising energy consumption valuesalong with corresponding time and outside temperature values, where theenergy consumption values within the each of the plurality of energy usedata sets are shifted by one of a plurality of lag values relative tothe corresponding time and outside temperature values, and where each ofthe plurality of lag values is different from other ones of theplurality of lag values; performing a machine learning model analysis onthe each of the plurality of energy use data sets to yield correspondingmachine learning model parameters and a corresponding residual;determining a least valued residual from all residuals yielded by themachine learning model analysis, the least valued residual indicating acorresponding energy lag for the each of the buildings, where thecorresponding energy lag describes a transient energy consumption periodpreceding a change in outside temperature; using outside temperatures,the machine learning model parameters, and energy lags for all of thebuildings to estimate a cumulative energy consumption for the buildings,and to predict a dispatch order reception time for the demand responseprogram event; and employing the dispatch order reception time toprepare actions required to control the each of the buildings tooptimally shed energy specified in a corresponding dispatch order.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features, and advantages of the presentinvention will become better understood with regard to the followingdescription, and accompanying drawings where:

FIG. 1 is a timing diagram illustrating two present day energyconsumption profiles for an exemplary building indicating electricityconsumed by various components within the building at a level ofgranularity approximately equal to one week;

FIG. 2 is a diagram depicting a present day coarse-grained 5-parameterregression baseline model showing energy consumption of the exemplarybuilding as a function of outside temperature, and which is derived fromthe two energy consumption profiles of FIG. 1;

FIG. 3 is a block diagram featuring an exemplary present day 5-parameterregression baseline model for the building of FIG. 1, which is derivedfrom an exemplary energy consumption profile consisting of fine-grainedenergy consumption data;

FIG. 4 is a block diagram showing a fine-grained baseline energy dataweather normalization apparatus according to the present invention;

FIG. 5 is a diagram illustrating a fine-grained baseline energy dataweather normalization method according to the present invention;

FIG. 6 is a block diagram detailing a weather induced facility energyconsumption characterization system according to the present invention;

FIG. 7 is a block diagram illustrating a demand response dispatch systemaccording to the present invention;

FIG. 8 is a block diagram depicting a demand response dispatchprediction system according to the present invention; and

FIG. 9 is a block diagram featuring a brown out prediction systemaccording to the present invention.

DETAILED DESCRIPTION

Exemplary and illustrative embodiments of the invention are describedbelow. In the interest of clarity, not all features of an actualimplementation are described in this specification, for those skilled inthe art will appreciate that in the development of any such actualembodiment, numerous implementation specific decisions are made toachieve specific goals, such as compliance with system related andbusiness-related constraints, which vary from one implementation toanother. Furthermore, it will be appreciated that such a developmenteffort might be complex and time consuming but would nevertheless be aroutine undertaking for those of ordinary skill in the art having thebenefit of this disclosure. Various modifications to the preferredembodiment will be apparent to those skilled in the art, and the generalprinciples defined herein may be applied to other embodiments.Therefore, the present invention is not intended to be limited to theparticular embodiments shown and described herein but is to be accordedthe widest scope consistent with the principles and novel featuresherein disclosed.

The present invention will now be described with reference to theattached figures. Various structures, systems, and devices areschematically depicted in the drawings for purposes of explanation onlyand so as to not obscure the present invention with details that arewell known to those skilled in the art. Nevertheless, the attacheddrawings are included to describe and explain illustrative examples ofthe present invention. The words and phrases used herein should beunderstood and interpreted to have a meaning consistent with theunderstanding of those words and phrases by those skilled in therelevant art. No special definition of a term or phrase (i.e., adefinition that is different from the ordinary and customary meaning asunderstood by those skilled in the art) is intended to be implied byconsistent usage of the term or phrase herein. To the extent that a termor phrase is intended to have a special meaning (i.e., a meaning otherthan that understood by skilled artisans), such a special definitionwill be expressly set forth in the specification in a definitionalmanner that directly and unequivocally provides the special definitionfor the term or phrase.

In view of the above background discussion on building energyconsumption and associated present day techniques employed to developweather normalized energy consumption baselines that allow for analysesof building energy use, a discussion of the present-day techniques andtheir limitations and disadvantages will now be presented with referenceto FIGS. 1-3. Following this, a discussion of the present invention willbe presented with reference to FIGS. 4-9. The present inventionovercomes the below noted limitations and disadvantages of present daytechniques, and others, by providing an apparatus and method that allowsfor derivation of multiple parameter baseline regression models fromfine-grained building energy use data in a manner that is exceedinglymore accurate that that which has heretofore been provided.

Turning to FIG. 1, a timing diagram 100 is presented illustrating twopresent day energy consumption profiles 101-102 for an exemplarybuilding, indicating electricity consumed by various components withinthe building at a level of granularity approximately equal to one week.The diagram 100 depicts approximate weekly energy consumption 101 overthe course of a first year and approximate weekly energy consumption 102over the course of a second year. Those skilled in the art willappreciate that the two profiles 101-102 are referred to as “baselineenergy consumption data” or “baseline data” for the exemplary building.The baseline data does not necessarily have to span a complete year, nordoes it have to be at a granularity of one week, though to establish acredible baseline for energy consumption and further modeling andanalysis, it is desirable to have a sufficient number of data points soas to fully characterize the span of energy consumption over variousweather, occupancy, and other conditions.

Going forward, energy consumption and associated discussions will employterms corresponding to electrical energy usage (e.g., kilowatts,kilowatt hours) because electrical energy usage and derivation ofelectrical usage baselines are currently more prevalent in the art.However, the present inventors note that the principles and techniquesdisclosed herein according to the present invention are equallyapplicable to other forms of energy such as, but not limited to, water,natural gas, fossil fuels, and nuclear fuels.

Consider profile 101, where roughly 10 kilowatt hours (kWh) ofelectricity are consumed by the exemplary building during the weeks inJanuary of the first year, decreasing down to a low of roughly 5 kWhduring the weeks surrounding March of the first year, and increasing andpeaking to slightly under 20 kWh during the summer months, decreasing toa low usage of roughly 5 kWh in the fall, and increasing up to roughly10 kWh as it turns cold in the fall. Profile 101 is typical of manysmall- to medium-sized buildings (SMBs) in various locations in theworld. These SMBs may comprise heating, ventilation, andair-conditioning (HVAC) systems to control climate within the SMBs at acomfort level supporting occupancy. Such is found in factories, schools,churches, airports, office buildings, etc. The HVAC systems may be verysimple, and thermostat controlled, or they may be part of more complexbuilding management systems (BMSs) that may include occupancy sensors,controlled lighting, and mechanisms to actively manage building energyuse by varying activation schedules and/or duty cycles of equipment(e.g., compressors, evaporators, condensers, fans, lights, etc.).

Given that electrical energy is generally purchased from a utilityprovider (e.g. Tennessee Valley Authority), profile 101 implies thatthere are costs associated with heating the exemplary building thatincrease during colder weather and that decrease following the colderweather as seasonal temperatures increase. Profile 101 also indicatesthat there are costs associated with cooling the exemplary building thatincrease during warmer weather and that decrease following the warmerweather as seasonal temperatures decrease. But heating and cooling costsare not the only components of overall building energy consumption as isshown in profile 101. Other components may be due to energy use as afunction of, but not limited to, occupancy, usage of large equipment,lighting, hours of operation, and equipment maintenance or malfunctionissues. It is those other components of energy use that are of interest,in addition to energy use as a function of weather, to the presentapplication. Yet, without installing costly and complex energymonitoring equipment within the exemplary facility, it is difficult atbest to separate energy consumption as a function of the weather (i.e.,“weather induced energy consumption”) from the other components ofenergy use.

To complicate matters, one skilled in the art will also appreciate thatweather is not the same from year to year. Accordingly, profile 102 ispresented as an example of energy consumption by the same exemplarybuilding in a second year, where it is presumed that the configurationand use of the exemplary building may or may not be different in thefirst and second years. Yet, a building manager, utility grid manager,or energy service company analyst cannot discern the impact of otherenergy efficiency or demand reduction mechanisms on the building'senergy use without first estimating the effects of weather on thebuilding's energy use, subtracting those effects from the overall usageprofiles 101-102, and normalizing those profiles 101-102 to a referenceoutside temperature (e.g., 65 degrees Fahrenheit (F)) so that theprofiles 101-102 can be compared in a manner that will yield meaningfulresults. At this point, all that can be derived from the profiles101-102 of FIG. 1 is that weather in the second year may have beenmilder that the weather patterns of the first year.

To address the uncertainties associated with determining how much energyan exemplary building should be consuming as a function of weather(namely, outside air temperature), those within the art have fieldedstandard techniques for estimating the effects of weather on abuilding's energy consumption, one of which is specified in Measurementof Energy and Demand Savings, ASHRAE Guideline 14-2002, published in2002 by The American Society of Heating, Refrigerating andAir-Conditioning Engineers, Inc. It is not the intent of the presentapplication to provide an in-depth discussion of the differenttechniques for estimating weather induced energy consumption effects,for this will be evident to those of skill in the art. What issufficient to note herein is the essence of these techniques and theirlimitations when employed to estimate weather induced energy consumptioneffects using fine-grained energy consumption data.

One skilled in the art will further appreciate that there are manyapplications for a baseline energy consumption model that is derivedfrom coarse-grained or fine-grained energy use data, such coarse-graineddata as is represented by the profiles 101-102 of FIG. 1. Once anaccurate model of weather induced energy consumption effects has beenderived from the profiles 101-102, the model may be employed, amongother purposes, to allow for meaningful comparisons of energy usage fromperiod to period (e.g., year to year, month to month, etc.), it may beemployed to validate data corresponding to demand reduction or energyefficiency programs, or it may be employed to predict future consumptionas a function of weather.

Now turning to FIG. 2, a diagram 200 depicting a present daycoarse-grained 5-parameter regression baseline model 201 is presentedshowing energy consumption of the exemplary building as a function ofoutside temperature (“weather”). The 5-parameter regression baselinemodel 201 is derived from the two energy consumption profiles 101-102 ofFIG. 1. The model 201 includes a linear heating component 202 that ischaracterized by an intercept A and a heating slope B. The model 201also has a baseline consumption component 203 that is characterized byheating change point C and cooling change point E. The model 201 furtherincludes a linear cooling component 204 that is characterized by acooling slope D. Also shown in the diagram 200 is a shaded distributionarea 205 that depicts the distribution boundaries of the energyconsumption values of either of the profiles 101-102, or of the energyconsumption values of both of the profiles 101-102, depending upon thebaseline data that is employed in the 5-parameter regression analysisthat results in the model 201 itself. Generally speaking, as one skilledin the art will appreciate, the accuracy of the model 201 is increasedin correspondence to the amount of baseline energy consumption data thatis used to develop the model 201 via the regression analysis. Thetemperatures depicted on the axis labeled OUTSIDE TEMP represent averageoutside temperature for each of the weeks of FIG. 1. For example, weeksin which the average temperature is 55 degrees have their correspondingbuilding energy use values distributed within the shaded area at the55-degree mark. Weeks in which the average temperature is 75 degreeshave their corresponding building energy use values distributed withinthe shaded area at the 75-degree mark. And so on. The heating component202, baseline consumption component 203, and cooling component 204, andtheir corresponding parameters A-E and derived by performing the5-parameter regression analysis to minimize the residual error term(typically mean squared error between estimate and actual data points).Thus, the model 201 represents a minimized-residual 5-parameter equationthat may be employed to generate an estimate of energy consumption bythe exemplary building for a given outside temperature. For instance,the model 201 indicates that for a week having an average outsidetemperature of 30 degrees, A kWh will be consumed. In actuality,building energy consumption on 30-degree average temperature weeksvaries about parameter A as bounded by the shaded distribution area 205,but the variance about A of the baseline energy consumption data pointsused to develop the baseline model 201 is acceptable and sufficient tobe employed for purposes of weather normalization, use estimation, useprediction, and validation of energy demand or energy efficiency programcompliance.

Accordingly, the profiles 101-102 of FIG. 1 may be normalized to, say,65 degrees, by subtracting from the weekly energy consumption a modelestimate of energy use at the true average weekly temperature, andadding back a model estimate of energy consumption at 65 degrees. Thesesteps are performed for each of the weekly kWh values in both profiles101-102 according to the equation:Ē(i)=E(i)−M[T(i)]+M[T _(REF)], where:Ê(i) is an estimated weather normalized energy consumption for week i,E(i) is the actual energy consumption for week i, T(i) is the averageweekly temperature for week i, M[T(i)] is the model estimate of energyconsumption for the average weekly temperature T(i), T_(REF) is areference average weekly temperature, and M[T_(REF)] is the modelestimate of energy consumption for average weekly temperature T_(REF).

Thus, to normalize profiles 101-102 to 65 degrees, 65 is employed in themodel 201 as T_(REF), yielding two energy use profiles that arenormalized to 65 degrees, which can be compared or employed in otheruseful analyses. In other words, weather induced effects have beenremoved from the baseline energy use profiles 101-102 afternormalization to 65 degrees.

The present inventors note, however, that the regression model 201 ofFIG. 2 is prevalently employed today, but variations may also beemployed to include 4-parameter models where no baseline component 203is present, or they may also include occupancy effects, which are notincluded in the present discussion for clarity sake. Variations may alsoemploy well known heating degree days and cooling degree days in lieu ofaverage temperatures, but those variations are not particularly relevantfor purposes of the present application.

It is also noted that the granularity of baseline energy consumptiondata may be varied as well to develop a regression model. For example,rather than employing weekly energy consumption values to develop abaseline model for normalization purposes, monthly or daily values maybe employed as well to provide insight into energy consumption of theexemplary building at a granularity that is required for a givenanalysis application.

The present inventors have observed, though, that the present-daymodeling techniques discussed above with reference to FIGS. 1-2 workwell and are widely accepted within the art when they are employed usingbaseline energy consumption data having granularities of one day orgreater, however, when energy use data points having granularities lessthan one day are employed, the resulting models are quite useless andmisleading. These problems are more specifically discussed withreference to FIG. 3.

Referring to FIG. 3, a block diagram 300 is presented featuring anexemplary present day 5-parameter regression baseline model 301 for theexemplary building of FIG. 1, which is derived from an exemplary energyconsumption profile (not shown) consisting of fine-grained energyconsumption data, that is, energy consumption data that is obtained atintervals generally less than one day (e.g., every 12 hours, every 3hours, every hour). The model 301 includes a linear heating component302 that is characterized by an intercept A and a heating slope B. Themodel 301 also has a baseline consumption component 303 that ischaracterized by a heating change point C and a cooling change point E.The model 301 further includes a linear cooling component 304 that ischaracterized by a cooling slope D. Also shown in the diagram 300 is ashaded distribution area 305 that depicts the distribution boundaries ofthe energy consumption values obtained for the exemplary building and,in contrast to the shaded distribution area 205 of FIG. 2, thedistribution area 305 of FIG. 3 shows that the baseline energyconsumption data used to generate the model 301 varies substantiallyfrom the model 301 itself. And the present inventors have observed thatno amount of energy consumption data taken at a fine granularity willimprove the accuracy of the model 301, primarily because thedistribution of baseline energy consumption values for any of theoutside temperatures appears as noise, which conceals any accurate modelparameters that may be characterized therein.

Consequently, even though conventional weather normalization regressiontechniques have proved accurate and useful when employed to derivebaseline models from coarse-grained energy use data (i.e., data withgranularity equal to or greater than 24 hours), they utterly fail toyield model parameters that can be used to reliably and accuratelyestimate building energy consumption as a function of outside airtemperature. This is a significant problem, for use of such a model,like model 301, to normalize fine-grained energy use data for purposesof comparison, estimation, or prediction, will result in gross error.

The present inventors have further observed that present day weathernormalization techniques, such as those discussed above with referenceto FIGS. 1-3, are limiting in that they do not take in to considerationthe energy lag of a building. Not to be confused with thermal lag, whichdescribes a body's thermal mass with respect to time, energy lagaccording to the present invention describes a building's transientenergy consumption characteristics over time as a function of outsidetemperature. More specifically, a building's energy lag is the timerequired for the building's energy consumption to go through a transientenergy consumption response in order to reach its steady state energyconsumption. As one skilled in the art will concur, the energy lag of abuilding is not just associated with thermal mass but is also a functionof its internal HVAC components and building management system, whichoperate to optimize energy consumption. In other words, the presentinventors have noted that conventional weather normalization techniquesare accurate and useful as long as the intervals of energy use dataobtained are greater than the energy lag of a given building, becauseonly steady state energy consumption effects are comprehended by presentday normalization mechanisms. However, when the energy lag of a buildingis greater than the interval at which energy data is obtained, accuracyand reliability of a model 301 derived via conventional normalizationmechanisms is substantially decreased to the point of uselessness.Although the above noted period of time is referred to as an energy lag,the present inventors note that the value of this period may be positiveor negative. For example, a positive energy lag would describe abuilding having a transient energy consumption period following a changein outside temperature. Alternatively, a negative energy lag woulddescribe a building having a transient energy consumption periodpreceding a change in outside temperature. Although not common,buildings having negative energy lags may often comprise HVAC componentsthat perform, say, preemptive cooling or heating.

The present invention overcomes the above noted limitations anddisadvantages of the prior art, and others, by providing apparatus andmethods for characterizing and creating accurate and reliable models ofbuilding energy consumption that are derived from fine-grained energyconsumption data, namely, data obtained at intervals which are less thanthe energy lag of the building under consideration. For most SMBs,intervals on the order of one hour would otherwise result in the noisydistribution area 305 of FIG. 3 because the energy lag of such SMBs isgreater than one hour, though the present inventors also note that anoisy distribution 305 may also result from using baseline data having24-hour (or greater) granularity for extremely large facilities (e.g.,enclosed stadiums), or facilities having very inefficient energy useresponses to changes in outside temperature.

The present inventors have further observed that when energy consumptiondata is shifted in time relative to outside temperature data in abaseline at an amount approximately equal to a building's energy lag,and when regression analyses are performed on this shifted data, noisydistribution areas such as area 305 of FIG. 3 tend to converge toboundaries approaching acceptable amounts, such as area 205 of FIG. 2.Accordingly, it is an objective of the present invention to determine abuilding's energy lag and to employ its energy lag when generatingweather normalization model parameters. The present invention will nowbe discussed with reference to FIGS. 4-9.

Referring now to FIG. 4, a block diagram is presented showing afine-grained baseline energy data weather normalization apparatus 400according to the present invention. The apparatus 400 includes abaseline data stores 401 that is coupled to a building lag optimizer410. The optimizer 410 includes a thermal response processor 411 and aregression engine 412. The processor 411 is coupled to the regressionengine 412 via a thermal lag bus THERMLAG, a lag data bus LAGDATA, and aresidual bus RESIDUAL. The lag optimizer 410 generates outputs signalsindicating values on an optimum lag bus OPTLAG and on an optimumparameters bus OPTPAR.

The baseline data stores 401 comprises fine-grained baseline energyconsumption data corresponding to one or more buildings (or,“facilities”), where there is a sufficient amount of consumption datafor each of the one or more buildings to enable an accurate energyconsumption baseline regression model to be generated for each of theone or more buildings. In one embodiment, granularity of fine-grainedbaseline energy consumption data corresponding to some of the one ormore buildings is one hour. In another embodiment, granularity offine-grained baseline energy consumption data corresponding to some ofthe one or more buildings is 15 minutes. Other embodiments contemplate acombination of intervals that would be construed as “fine-grained”according to the present disclosure, such as 24-hour interval data forbuildings having energy lags greater than 24 hours. Further embodimentscomprehend fine-grained energy consumption data that differs in intervalsize from building to building within the stores 401. In one embodiment,the stores 401 may be collocated with the building lag optimizer 410such as, but not limited to, within a network operations center (NOC)corresponding to an energy service company, an independent systemoperator (ISO), a regional transmission organization (RTO), atransmission system operator (TSO), or any of a number of other concernsthat control and monitor operation of an electricity transmission grid.Other embodiments of the present invention contemplate deployment of theapparatus 400 within like-functioned facilities corresponding to controland monitoring of other energy sources as noted above. In allembodiments, the baseline energy consumption data for each of the one ormore buildings comprises a time of day value or other type or value fromwhich granularity of the baseline energy consumption data may bedetermined. Likewise, all embodiments comprise an outside temperaturevalue corresponding to each data point of energy consumption for each ofthe one or more buildings.

In operation, the regression engine 412 functions to generate energyconsumption baseline regression models characterized by model parametersfor one or more fine-grained baseline energy consumption profiles, asare discussed above. Embodiments of the regression engine 412 comprehenda 5-parameter multivariable regression model that minimizes its residualterm, or a 4-parameter multivariable regression model that minimizes itsresidual term, or other multivariable regression techniques that areknown in the art for use in developing baseline energy consumptionmodels. In one embodiment, the regression engine 412 may comprise acombination of the above noted regression models.

Baseline energy consumption data for a selected one of the one or morebuildings may be downloaded to the thermal response processor 411 or theconsumption data may be streamed over a network of interconnectionsknown in the art. In addition, the thermal response processor 411 may beconfigured to accept downloaded or streamed data for a plurality of theone or more buildings simultaneously and may be employed to control theregression engine 412 for purposes of determining an optimal energy lagfor one or each of the plurality of the one or more buildings whosebaseline energy consumption data are obtained via the stores 401. Forpurposes of clarity, operation of the thermal response processor 411will be discussed with reference to generation of an optimum energy lagand associated optimum regression model parameters corresponding to asingle one of the one or more buildings.

Responsive to baseline energy consumption data that is received from thebaseline data stores 401, the thermal response processor 411 providesthe data to the regression engine 412 over LAGDATA along with a firstvalue on THERMLAG that indicates an amount of time lag to shift energyconsumption data relative to time stamp and outside temperature valuesin the baseline data. The first value on THERMLAG may be a time of dayor may merely be an integer value indicating how many increments toshift energy consumption data so that it lags the time and temperaturevalues in the baseline data by that number of increments.

Upon reception of the baseline data on LAGDATA and a first lag value onTHERMLAG, the regression engine 412 performs a regression function asnoted above to generate first model parameters and a first residual fora first corresponding model to be employed for analysis purposes. Thefirst model parameters are output to bus OPTMPAR and the first residualis provided to the response processor 411 on bus RESIDUAL.

In a second iteration, the processor 411 generates a second value onTHERMLAG that results in a corresponding shift in the energy consumptiondata relative to the time stamp and outside temperature values of thebaseline data. The second value, in one embodiment, is an increment ofthe first value. Responsively, the regression engine 412 generatessecond model parameters and a second residual for a second correspondingmodel to be employed for analysis purposes. The second model parametersare output to bus OPTMPAR and the second residual is provided to theresponse processor 411 on bus RESIDUAL.

The iterations of energy lag continue, with generation of respectivemodel parameters, lag values, and residuals until a lag threshold hasbeen reached indicating that the baseline energy consumption data hasbeen shifted a number of increments greater than an estimated energy lagof the building being modeled. In an embodiment that is using energybaseline data in 1-hour increments, 24 iterations may be performed, thusgenerating 24 thermal lag values and 24 sets of regression modelparameters (e.g., parameters A-E in FIGS. 2-3).

Upon completion of the iterations, the thermal response processor 411compares all of the residuals generated by each of the above iterationsand determines which one of the residuals is less than all of the otherresiduals. The lag value that corresponds to the minimum value residualresulting from all of the above iterations corresponds to the energy lagof the building under consideration, and that lag value is output on busOPTLAG along with model parameters that were generated using that lagvalue.

The present inventors note that other embodiments of the building lagoptimizer 410 contemplate variations of the thermal response processor411 that perform shifting of the baseline consumption data itselfrelative to time stamp and temperature data, and that receives modelparameters from the regression engine 411, and which generates bothvalues on OPTLAG and OPTMPAR. Via such embodiments a conventionalregression engine 412 may be employed as opposed to one that performedthe additional functions of time shifting the energy consumption data.

The building lag optimizer 410 according to the present invention isconfigured to perform the functions and operations as discussed above.The optimizer 410 may comprise logic, circuits, devices, or applicationprograms (i.e., software) disposed within a non-transitory medium suchas a hard disk or non-volatile memory, or a combination of logic,circuits, devices, or application programs, or equivalent elements thatare employed to execute the functions and operations according to thepresent invention as noted. The elements employed to accomplish theseoperations and functions within the building lag optimizer 410 may beshared with other circuits, logic, etc., that are employed to performother functions and/or operations commensurate with intendedapplication.

Now turning to FIG. 5, a diagram 500 is presented illustrating afine-grained baseline energy data weather normalization method accordingto the present invention, such as may be employed in the building lagoptimizer 410 of FIG. 4. The diagram 500 depicts a plurality of timeshifted versions 501.1-501.N−1 of a portion of an exemplary energyconsumption baseline profile, where it is noted that the exemplaryenergy consumption baseline profile comprises a number of data pointsequal to or greater than 2N−1, and wherein successively increasingvalues of index correspond to later points in time. That is, forbaseline data having intervals of 1 hour, an index of 3 (e.g. I.3, T.3,E.3) comprises energy consumption data that is one hour later thanbaseline data having an index of 2 (e.g., I.2, T.2, E.2). Each of thetime shifted versions 501.1-501.N−1 comprises N time stamps I.1-I.N−1, Noutside temperature values T.1-T.N−1, and N energy consumption valuesE.X-E.X+N−1. A first time shifted version 501.1 comprises a 0-index timeshift in the portion of the originally obtained baseline energyconsumption data. A second time shifted version 501.2 comprises a1-index time shift. A third time shifted version 501.3 comprises a2-index time shift. And so on until an Nth time shifted version 501.Ncomprises an N−1-index time shift.

According to the present invention, a lag LAG.0-LAG.N−1 equal to thetime shift is recorded, and multiple regression model parametersMPAR.0-MPAR.N−1 and residuals RESID.0-RESID.N−1 are generated by theregression engine 412. The thermal response processor 411 then comparesall N residuals and selects the one having the least value as theoptimum residual. Accordingly, the lag value and model parameterscorresponding to the optimum residual are designated as the optimumenergy lag and optimum regression model parameters for the buildingunder consideration. Henceforth, when analyses are performed for thebuilding under consideration, the optimum regression model parametersand optimal energy lag are employed to perform weather normalizationcomparisons, efficiency analyses, consumption predictions, validations,etc.

The techniques discussed above with reference to FIGS. 4-5 disclosespecific embodiments for performing the functions required on one ormore sets of baseline energy consumption data in order to determine agiven building's energy lag along with optimum multiple regression modelparameters that may be employed to perform weather normalization andother useful applications. However, the present inventors note that thesteps described above are exemplary of other mechanisms that may beemployed to shift baseline data relative to outside temperature in orderto identify a building's energy lag (i.e., the time associated with aleast-valued residual within a plurality of residuals corresponding to aplurality of mutually exclusive shifts in time of the baseline data),and to derive therefrom optimal regression model parameters. What oneskilled in the art will appreciate from the above disclosure is thatessential features of the present invention are performing a pluralityof multiple variable regressions yielding a corresponding plurality ofresiduals, where each of the plurality of multiple variable regressionsis associated with a time shift of energy consumption baseline data thatis mutually exclusive of remaining time shifts associated with remainingmultiple variable regressions within the plurality of multiple variableregressions.

The present inventors also note that multiple variable regressionanalysis techniques are presented above with reference to the presentinvention in order to teach relevant aspects using prevalently knownmechanisms in the art. However, it is noted that the present inventionmay also be embodied within configurations that utilize techniques otherthan multiple variable regression analysis in order to derive modelingparameters that accurately characterize a building's energy consumptionand energy lag. Such techniques may include, but are not limited to, asone skilled in the art will appreciate, more than one statisticaltechnique may be used to produce an approximation of a building'sdependent energy relation with weather and lag. Any such technique isformalized as a function of independent parameters that describebaseline energy consumption data for the building relative to outsidetemperature and unknown mutually exclusive shifts in time of thebaseline data's parameters. The difference between observed values ofenergy consumption and estimated values of energy consumption associatedwith such a function is a quantity that one skilled in the art seeks tominimize in order to improve model accuracy. Although the residuals areemployed herein as a measure of goodness-of-fit, the present inventorsnote that other variables are contemplated by the present inventionwhich include, but are not limited to, linear models of more or lessparameters, non-linear models of a parabolic or higher polynomial orderas well as machine learning modeling techniques (e.g., neural-networks,decision trees, etc.).

In addition to the embodiments discussed above with reference to FIGS.4-5, the present invention may also be configured to perform useful andvaluable functions when applied to other embodiments, which will bedescribed below with reference to FIGS. 6-9.

Referring now to FIG. 6, a block diagram is presented detailing aweather induced facility energy consumption characterization system 600according to the present invention. The system 600 includes a facilitystores 601 that is coupled to a facility processor 602 via a firstfacility data bus FACDATA1 and a facility optimal features bus FACOPT.The facility processor 602 is coupled to a building lag optimizer 603via a second facility data bus FACDATA2, an optimal lag bus OPTLAG, andan optimal model parameters bus OPTMPAR. The building lag optimizer 603is coupled to a baseline data stores 604.

The facility stores 601 comprises identification data corresponding toone or more buildings to allow for characterization data to beassociated therewith, and to allow for selection of correspondingbaseline energy consumption data that is stored in the baseline datastores 604. In one embodiment, the baseline data stores 604 comprisesfine-grained baseline energy consumption data corresponding to the oneor more buildings as described earlier, and functions in substantiallythe same manner as the baseline data stores 401 described above withreference to FIG. 4. In another embodiment, the facility data stores 601and baseline data stores 604 may share common hardware and software forarchival and access purposes.

Operationally, the characterization system 600 is employed to determinecharacterizing features of the one or more buildings which include, butare not limited to, energy lag and optimal multiple regression baselinemodel parameters as are described above. The facility processor 602retrieves facility data from the facility stores 601 for a selectedbuilding and provides this data to the lag optimizer 604 on FACDATA2.Responsively, the lag optimizer 603 retrieves one or more sets of energyconsumption baseline data corresponding to the selected building fromthe baseline data stores 604 and performs the functions described aboveto generate an energy lag for the building along with optimal modelparameters. The energy lag is provided to the facility processor 602 onbus OPTLAG and the optimal model parameters are provided on bus OPTMPAR.The facility processor 602 may subsequently select a second buildingfrom the one or more buildings and provide its data to the lag optimizer603 for generation of a second energy lag and second optimal modelparameters. The facility processor 602 may subsequently perform thesefunctions for remaining buildings in the facility stores 601. In oneembodiment, the facility processor 602 and building lag optimizer 603may perform the disclosed functions serially for each of the one or morebuildings. In another embodiment, the facility processor 602 and lagoptimizer 603 may perform the disclosed functions for a plurality of theone or more buildings concurrently.

In one embodiment, the facility processor 602 may provide the energylags and optimum model parameters for corresponding ones of the one ormore buildings to the facility stores 601 via FACOPT, where the energylags and optimum model parameters are stored and may be henceforthaccessed for employment in other applications.

In another embodiment, in addition to providing the energy lags andoptimum model parameters for corresponding ones of the one or morebuildings to the facility stores 601 via FACOPT, the facility processor602 may create categories of buildings that have been optimizedaccording to the above. The categories may correspond to a common energylag (e.g., 1-hour buildings, 2-hour buildings) or they may correspond toranges of energy lag (e.g., fast buildings (0-4 hours), nominalbuildings (5-8 hours), slow buildings (8-12 hours), etc.). The facilityprocessor 602 may provide these categories as well over FACOPT so thatbuildings having similar energy lags may be identified and theirparameters accessed for further application.

Advantageously, the system 600 according to FIG. 6 may be employed todetermine useful energy consumption attributes of the one or morebuildings without any knowledge whatsoever of the size of the one ormore buildings.

Turning now to FIG. 7, a block diagram is presented illustrating ademand response dispatch system 700 according to the present invention.The dispatch system 700 may include a dispatch processor 702 thatreceives a demand response dispatch order from a dispatch authority suchas an ISO, RTO, or utility. The dispatch order may specify, among otherthings, a future time to execute a demand response program event alongwith a value of energy that is to be shed by participants in acorresponding demand response program. The dispatch processor 702 iscoupled to participant stores 701 via a first facility data bus FACDATA1and to a dispatch control element 705 via bus DSCHED.

The dispatch processor 702 is coupled to a building lag optimizer 703via a second facility data bus FACDATA2, an optimal lag bus OPTLAG, andan optimal model parameters bus OPTMPAR. The building lag optimizer 703is coupled to a baseline data stores 704.

The participant stores 701 comprises identification data correspondingto one or more buildings that participate in the demand response programto allow for selection of corresponding baseline energy consumption datathat is stored in the baseline data stores 704, to allow for energy lagsto be associated therewith, and to further allow for employment of theenergy lags in development of a schedule for dispatch control for eachof the participants in the program event. In one embodiment, thebaseline data stores 704 comprises fine-grained baseline energyconsumption data corresponding to the one or more buildings as describedearlier, and functions in substantially the same manner as the baselinedata stores 401 described above with reference to FIG. 4. In anotherembodiment, the participant data stores 701 and baseline data stores 704may share common hardware and software for archival and access purposes.

Operationally, the demand response dispatch system 700 is employed todetermine energy lags of the one or more buildings that participate inthe demand response program corresponding to the dispatch order. Thesystem 700 also generates a dispatch schedule and performs dispatch foreach of the one or more buildings to optimally shed the energy specifiedin the dispatch order in a timely manner. In one embodiment, thebuildings with the highest values of energy lag are dispatched uponcommencement of the program event because these buildings are presumedto exhibit a longer transient energy consumption response to changes inoutside temperature, and thus they may exhibit a longer transientinternal temperature response to abrupt changes in energy consumption,which may achieve demand response program event objectives whilepreserving comfort levels for internal occupants. Subsequent dispatchesare performed in order of decreasing energy lags. One advantage ofprioritizing dispatches in the noted order is that the effects of theload shedding for high energy lag buildings may not affect comfort ofthe occupants therein. The present inventors note, however, that ifduration of the program event is longer than energy lags of some of thebuildings participating in the program event, comfort levels may beaffected.

To generate the above prioritized dispatch schedule, the dispatchprocessor 702 retrieves facility data from the participant stores 701for a selected building and provides this data to the lag optimizer 703on FACDATA2. Responsively, the lag optimizer 703 retrieves one or moresets of energy consumption baseline data corresponding to the selectedbuilding from the baseline data stores 704 and performs the functionsdescribed above to generate an energy lag for the building along withoptimal model parameters. The energy lag is provided to the dispatchprocessor 702 on bus OPTLAG and the optimal model parameters areprovided on bus OPTMPAR. The dispatch processor 702 may subsequentlyselect a second building from the one or more buildings and provide itsdata to the lag optimizer 703 for generation of a second energy lag andsecond optimal model parameters. The dispatch processor 702 maysubsequently perform these functions for remaining buildings in theparticipant stores 701. In one embodiment, the dispatch processor 702and building lag optimizer 703 may perform the disclosed functionsserially for each of the one or more buildings. In another embodiment,the dispatch processor 702 and lag optimizer 703 may perform thedisclosed functions for a plurality of the one or more buildingsconcurrently.

In one embodiment, the dispatch processor 702 may employ the energy lagsfor corresponding ones of the one or more buildings to generate adispatch schedule where buildings with greater energy lags aredispatched prior to buildings with lesser energy lags. The dispatchschedule is provided to the dispatch control 705 via DSCHED. Uponcommencement of the program event, the dispatch control 705 controls thespecified load shedding by performing load shedding actions in the orderprovided for by the dispatch schedule.

Referring to FIG. 8, a block diagram is presented depicting a demandresponse dispatch prediction system 800 according to the presentinvention. The dispatch prediction system 800 may include a dispatchprediction element 802 that is configured to predict a first future timewhen a demand response dispatch order may be received from a dispatchauthority such as an ISO, RTO, or utility. The dispatch order mayspecify, among other things, a second future time to execute a demandresponse program event along with a value of energy that is to be shedby participants in a corresponding demand response program. The dispatchprediction element 802 is coupled to participant stores 801 via a firstfacility data bus FACDATA1 and to a dispatch control element 805 via busDISPTIME. The dispatch prediction element 802 is also coupled to weatherstores 806 via bus WDATA.

The dispatch prediction element 802 is coupled to a building lagoptimizer 803 via a second facility data bus FACDATA2, an optimal lagbus OPTLAG, and an optimal model parameters bus OPTMPAR. The buildinglag optimizer 803 is coupled to a baseline data stores 804.

The weather stores 806 comprises weather predictions that includeoutside temperatures corresponding to one or more buildings that arestored in the participant stores 801. The weather stores 806 may belocated on site or may be located remotely and accessed via conventionalnetworking technologies.

The participant stores 801 comprises identification data correspondingto one or more buildings that participate in the demand response programto allow for selection of corresponding baseline energy consumption datathat is stored in the baseline data stores 804, to allow for energy lagsand optimal regression model parameters to be associated therewith, andto further allow for employment of the energy lags and optimalregression model parameters, in conjunction with predicted outsidetemperatures provided via the weather stores 806, to estimate the firstfuture time when the dispatch order is expected to be received fordispatch control of each of the participants in the program event. Inone embodiment, the baseline data stores 804 comprises fine-grainedbaseline energy consumption data corresponding to the one or morebuildings as described earlier, and functions in substantially the samemanner as the baseline data stores 401 described above with reference toFIG. 4. In another embodiment, the participant data stores 801 andbaseline data stores 804 may share common hardware and software forarchival and access purposes.

Operationally, the demand response dispatch prediction system 800 isemployed to estimate cumulative energy consumption as a function of thepredicted outside temperatures occurring in a timeline for all of theone or more buildings that participate in the demand response program,where energy lags according to the present invention are utilized ingeneration of a cumulative energy consumption timeline. It is notedthat, according to features of the present invention disclosed herein,the predicted energy consumption timeline may be employed to anticipatereception of a dispatch order to a finer level of granularity than thatwhich has heretofore been provided. By using the energy lags associatedwith the buildings in the participant stores 801, estimated reception ofa dispatch may be fine tuned. That is, using conventional dispatchprediction mechanisms that do not take into account energy lags ofprogram participants may result in predicted dispatch reception timesthat are much earlier than necessary. Advantageously, by utilizing thepresent invention to determine a time when a dispatch threshold ofenergy consumption will be reached due to outside temperature, an energyservices company or other demand response dispatch control entity may beprovided with, say, additional hours for preparation of dispatch controlactions.

The system 800 generates a predicted dispatch time that is provided tothe dispatch control 805 for preparation of actions required to controleach of the one or more buildings to optimally shed the energy specifiedin the dispatch order, upon reception of the dispatch order.

To predict the dispatch time, the dispatch prediction element 802retrieves facility data from the participant stores 801 for a selectedbuilding and provides this data to the lag optimizer 803 on FACDATA2.Responsively, the lag optimizer 803 retrieves one or more sets of energyconsumption baseline data corresponding to the selected building fromthe baseline data stores 804 and performs the functions described aboveto generate an energy lag for the building along with optimal modelparameters. The energy lag is provided to the dispatch predictionelement 802 on bus OPTLAG and the optimal model parameters are providedon bus OPTMPAR. The dispatch prediction element 802 subsequently selectsa second building from the one or more buildings and provides its datato the lag optimizer 803 for generation of a second energy lag andsecond optimal model parameters. The dispatch prediction element 802 maysubsequently perform these functions for remaining buildings in theparticipant stores 801. In one embodiment, the dispatch predictionelement 802 and building lag optimizer 803 may perform the disclosedfunctions serially for each of the one or more buildings. In anotherembodiment, the dispatch prediction element 802 and lag optimizer 803may perform the disclosed functions for a plurality of the one or morebuildings concurrently.

Once the energy lags and optimal model parameters have been generatedfor all of the buildings in the participant stores 801, the dispatchprediction element 802 accesses the weather stores 806 to obtain futureoutside temperatures corresponding to each of the one or more buildingsfor a specified future time period. The dispatch predication element 802then builds a cumulative future energy consumption timeline for all ofthe buildings using the outside temperatures as inputs to energyconsumption models according to the present invention for all of thebuildings. The dispatch prediction element 802 then processes thecumulative energy consumption timeline to determine a time whencumulative energy consumption increases as to cross a specifiedthreshold known to trigger a demand response program event. The point atwhich consumption crosses the specified threshold is tagged as adispatch time. From the dispatch time, the dispatch prediction element802 may utilize demand response program contract data stored therein tocalculate a predicted dispatch reception time, typically 24 hours priorto commencement of the demand response program event. The dispatchreception time is provided to the dispatch control element 805 on busDISPTIME to allow for commencement of dispatch actions at a time havinggreater accuracy than that which has heretofore been provided.

Finally turning to FIG. 9, a block diagram is presented featuring abrown out prediction system 900 according to the present invention. Thebrown out prediction system 900 may include a peak prediction element902 that is configured to predict a future brown out time when energyconsumption on a grid controlled by an ISO, RTO, or utility, may exceednormal production capacity, and would thereby require exceptionalmeasures known in the art to increase energy capacity. The peakprediction element 902 is coupled to grid stores 901 via a firstfacility data bus FACDATA1 and to a peak control element 905 via busBOTIME. The peak prediction element 902 is also coupled to weatherstores 906 via bus WDATA.

The peak prediction element 902 is coupled to a building lag optimizer903 via a second facility data bus FACDATA2, an optimal lag bus OPTLAG,and an optimal model parameters bus OPTMPAR. The building lag optimizer903 is coupled to a baseline data stores 904.

The weather stores 906 comprises weather predictions that includeoutside temperatures corresponding to buildings that are stored in thegrid stores 901. The weather stores 906 may be located on site or may belocated remotely and accessed via conventional networking technologies.

The grid stores 901 comprises identification data corresponding tobuildings or aggregates of buildings that are part of the grid to allowfor selection of corresponding baseline energy consumption data that isstored in the baseline data stores 904, to allow for energy lags andoptimal regression model parameters to be associated therewith, and tofurther allow for employment of the energy lags and optimal regressionmodel parameters, in conjunction with predicted outside temperaturesprovided via the weather stores 906, to estimate the future brown outtime. For purposes of this discussion, aggregates of buildings maycorrespond to a unit of distribution over the grid such as, but notlimited to, an electrical substation. As the present invention has beenapplied above to determine energy lags and optimal model parametersassociated with single buildings, the present invention may also beapplied to groups of buildings, say, a plurality of houses and businessthat are all powered from the same substation. Accordingly, thesubstation itself may be treated as a building for purposes ofdetermining an energy lag and optimal model parameters. Henceforth, anaggregate of buildings will be simply referred to as a building.

In one embodiment, the baseline data stores 904 comprises fine-grainedbaseline energy consumption data corresponding to the buildings asdescribed earlier, and functions in substantially the same manner as thebaseline data stores 401 described above with reference to FIG. 4. Inanother embodiment, the grid stores 901 and baseline data stores 904 mayshare common hardware and software for archival and access purposes.

Operationally, the brown out prediction system 900 is employed toestimate cumulative energy consumption on the grid as a function of thepredicted outside temperatures occurring in a timeline for all of thebuildings within the grid, where energy lags according to the presentinvention are utilized in generation of a cumulative energy consumptiontimeline. It is noted that, according to features of the presentinvention disclosed herein, the predicted energy consumption timelinemay be employed to anticipate activation of the exceptional measures toa finer level of granularity than that which has heretofore beenprovided. By using the energy lags associated with the buildings in thegrid stores 901, estimated time of occurrence of exceeding nominalproduction capacity may be fine tuned. That is, using conventional brownout prediction mechanisms that do not take into account energy lags ofgrid consumers may result in predicted brown out times that are muchsooner than they actually occur. Advantageously, by utilizing thepresent invention to determine a time when a peak threshold of energyconsumption will be reached due to outside temperature, a grid controlentity may be provided with, say, additional hours to manage peakconsumption on the grid.

The system 900 generates a predicted brown out time that is provided tothe peak control 905 for preparation of exceptional measures required tomanage peak consumption.

To predict the brown out time, the peak prediction element 902 retrievesbuilding data from the grid stores 901 for a selected building andprovides this data to the lag optimizer 903 on FACDATA2. Responsively,the lag optimizer 903 retrieves one or more sets of energy consumptionbaseline data corresponding to the selected building from the baselinedata stores 904 and performs the functions described above to generatean energy lag for the building along with optimal model parameters. Theenergy lag is provided to the peak prediction element 902 on bus OPTLAGand the optimal model parameters are provided on bus OPTMPAR. The peakprediction element 902 subsequently selects a second building andprovides its data to the lag optimizer 903 for generation of a secondenergy lag and second optimal model parameters. The peak predictionelement 902 subsequently performs these functions for remainingbuildings in the grid stores 901. In one embodiment, the peak predictionelement 902 and building lag optimizer 903 may perform the disclosedfunctions serially for each of the one or more buildings. In anotherembodiment, the dispatch prediction element 902 and lag optimizer 903may perform the disclosed functions for a plurality of the one or morebuildings concurrently.

Once the energy lags and optimal model parameters have been generatedfor all of the buildings in the grid stores 901, the peak predictionelement 902 accesses the weather stores 902 to obtain future outsidetemperatures corresponding to each of the one or more buildings for aspecified future time period. The peak predication element 902 thenbuilds a cumulative future energy consumption timeline for all of thebuildings using the outside temperatures as inputs to energy consumptionmodels according to the present invention for all of the buildings. Thepeak prediction element 902 then processes the cumulative energyconsumption timeline to determine a time when cumulative energyconsumption increases as to cross a specified threshold known to triggerthe exceptional measures. The point at which consumption crosses thespecified threshold is tagged as a brown out time. The brown out time isprovided to the peak control element 905 on bus BOTIME to allow forcommencement of the exceptional measures at a time having greateraccuracy than that which has heretofore been provided.

Portions of the present invention and corresponding detailed descriptionare presented in terms of software, or algorithms and symbolicrepresentations of operations on data bits within a computer memory.These descriptions and representations are the ones by which those ofordinary skill in the art effectively convey the substance of their workto others of ordinary skill in the art. An algorithm, as the term isused here, and as it is used generally, is conceived to be aself-consistent sequence of steps leading to a desired result. The stepsare those requiring physical manipulations of physical quantities.Usually, though not necessarily, these quantities take the form ofoptical, electrical, or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

Throughout this disclosure, exemplary techniques and mechanisms havebeen employed in order to clearly teach features of the presentinvention. For instance, the thermal response processor discussed withreference to FIGS. 4-5 is described in terms of a line search to findthe optimal building energy lag, however the present inventors note thatthe present invention comprehends many other techniques for findingoptimal energy lag parameters that may be more efficiently employed inaccordance with system configuration. These techniques may include, butare not limited to, bisection methods, Newton's method, and thermalannealing methods.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise, or as is apparent from the discussion,terms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, a microprocessor, a central processingunit, or similar electronic computing device, that manipulates andtransforms data represented as physical, electronic quantities withinthe computer system's registers and memories into other data similarlyrepresented as physical quantities within the computer system memoriesor registers or other such information storage, transmission or displaydevices.

Note also that the software implemented aspects of the invention aretypically encoded on some form of program storage medium or implementedover some type of transmission medium. The program storage medium may beelectronic (e.g., read only memory, flash read only memory, electricallyprogrammable read only memory), random access memory magnetic (e.g., afloppy disk or a hard drive) or optical (e.g., a compact disk read onlymemory, or “CD ROM”), and may be read only or random access. Similarly,the transmission medium may be metal traces, twisted wire pairs, coaxialcable, optical fiber, or some other suitable transmission medium knownto the art. The invention is not limited by these aspects of any givenimplementation.

The particular embodiments disclosed above are illustrative only, andthose skilled in the art will appreciate that they can readily use thedisclosed conception and specific embodiments as a basis for designingor modifying other structures for carrying out the same purposes of thepresent invention, and that various changes, substitutions andalterations can be made herein without departing from the scope of theinvention as set forth by the appended claims.

What is claimed is:
 1. A demand response dispatch prediction system,comprising: baseline data stores, configured to store a plurality ofbaseline energy use data sets for buildings participating in a demandresponse program; a building lag optimizer, configured to receiveidentifiers for said buildings, and configured to retrieve saidplurality of baseline energy use data sets from said baseline datastores for said buildings, and configured to generate energy use datasets for each of said buildings, each of said energy use data setscomprising energy consumption values along with corresponding time andoutside temperature values, wherein said energy consumption valueswithin said each of said energy use data sets are shifted by one of aplurality of lag values relative to said corresponding time and outsidetemperature values, and wherein each of said plurality of lag values isdifferent from other ones of said plurality of lag values, andconfigured to perform a machine learning model analysis on said each ofsaid energy use data sets to yield corresponding machine learning modelparameters and a corresponding residual, and configured to determine aleast valued residual from all residuals yielded, said least valuedresidual indicating a corresponding energy lag for said each of saidbuildings, and machine learning model parameters that correspond to saidleast valued residual, and wherein said corresponding energy lagdescribes a transient energy consumption period preceding a change inoutside temperature; a dispatch prediction element, coupled to saidbuilding lag optimizer and to weather stores, configured to receive, foreach of said buildings, outside temperatures, said corresponding energylag, and said corresponding machine learning model parameters, andconfigured to estimate a cumulative energy consumption for saidbuildings, and configured to predict a dispatch order reception time fora demand response program event; and a dispatch control element, coupledto said dispatch prediction element, configured to receive said dispatchorder reception time, and configured to prepare actions required tocontrol said each of said buildings to optimally shed energy specifiedin a corresponding dispatch order.
 2. The system as recited in claim 1,wherein said plurality of lag values indicates shifts of said energyconsumption values to different time and outside temperature values. 3.The system as recited in claim 1, wherein said corresponding time valuesare less than or equal to said corresponding energy lag for said each ofsaid buildings.
 4. The system as recited in claim 1, wherein saidcorresponding time values comprise hourly values and said plurality oflag values spans a 24-hour period.
 5. The system as recited in claim 1,wherein said dispatch order reception time is predicted as a function ofdemand response contract data and a specified threshold known to triggersaid demand response program event.
 6. The system as recited in claim 1,wherein said cumulative energy consumption for said buildings comprisesa future energy consumption timeline as a function of said outsidetemperatures.
 7. The system as recited in claim 1, wherein said each ofsaid energy use data sets comprises a first portion of a correspondingeach of a plurality of baseline energy use data sets, and whereinrequired energy consumption values resulting from shifts are taken froma second portion of said corresponding each of a plurality of baselineenergy use data sets.
 8. A system for predicting a dispatch forbuildings participating in a demand response program event, the systemcomprising: baseline data stores, configured to store a plurality ofbaseline energy use data sets for the buildings; a building lagoptimizer, configured to determine an energy lag for one of thebuildings, said building lag optimizer comprising: a thermal responseprocessor, configured to generate a plurality of energy use data setsfor said one of the buildings, each of said plurality of energy use datasets comprising energy consumption values along with corresponding timeand outside temperature values, wherein said energy consumption valueswithin said each of said plurality of energy use data sets are shiftedby one of a plurality of lag values relative to said corresponding timeand outside temperature values, and wherein each of said plurality oflag values is different from other ones of said plurality of lag values;and a machine learning model engine, coupled to said thermal responseprocessor, configured to receive said plurality of energy use data sets,and configured to perform a machine learning model analysis on said eachof said plurality of energy use data sets to yield corresponding machinelearning model parameters and a corresponding residual; wherein saidthermal response processor determines a least valued residual from allresiduals yielded by said machine learning model engine, said leastvalued residual indicating said energy lag for said building, whereinsaid energy lag describes a transient energy consumption periodpreceding a change in outside temperature; a dispatch predictionelement, coupled to said building lag optimizer and to weather stores,configured to receive, for each of the buildings, outside temperatures,said corresponding energy lag, and said corresponding machine learningmodel parameters, and configured to estimate a cumulative energyconsumption for the buildings, and configured to predict a dispatchorder reception time for the demand response program event; and adispatch control element, coupled to said dispatch prediction element,configured to receive said dispatch order reception time, and configuredto prepare actions required to control said each of the buildings tooptimally shed energy specified in a corresponding dispatch order. 9.The system as recited in claim 8, wherein said plurality of lag valuesindicates shifts of said energy consumption values to different time andoutside temperature values.
 10. The system as recited in claim 8,wherein said corresponding time values are less than or equal to saidcorresponding energy lag for said each of said buildings.
 11. The systemas recited in claim 8, wherein said corresponding time values comprisehourly values and said plurality of lag values spans a 24-hour period.12. The system as recited in claim 8, wherein said dispatch orderreception time is predicted as a function of demand response contractdata and a specified threshold known to trigger said demand responseprogram event.
 13. The system as recited in claim 8, wherein saidcumulative energy consumption for said buildings comprises a futureenergy consumption timeline as a function of said outside temperatures.14. The system as recited in claim 8, wherein said each of saidplurality of energy use data sets comprises a first portion of acorresponding each of a plurality of baseline energy use data sets, andwherein required energy consumption values resulting from shifts aretaken from a second portion of said corresponding each of a plurality ofbaseline energy use data sets.
 15. A method for dispatching buildingsparticipating in a demand response program event, the method comprising:retrieving a plurality of baseline energy use data sets for thebuildings from a baseline data stores; generating a plurality of energyuse data sets for each of the buildings, each of the plurality of energyuse data sets comprising energy consumption values along withcorresponding time and outside temperature values, wherein the energyconsumption values within the each of the plurality of energy use datasets are shifted by one of a plurality of lag values relative to thecorresponding time and outside temperature values, and wherein each ofthe plurality of lag values is different from other ones of theplurality of lag values; performing a machine learning model analysis onthe each of the plurality of energy use data sets to yield correspondingmachine learning model parameters and a corresponding residual;determining a least valued residual from all residuals yielded by themachine learning model analysis, the least valued residual indicating acorresponding energy lag for the each of the buildings, wherein thecorresponding energy lag describes a transient energy consumption periodpreceding a change in outside temperature; using outside temperatures,the machine learning model parameters, and energy lags for all of thebuildings to estimate a cumulative energy consumption for the buildings,and to predict a dispatch order reception time for the demand responseprogram event; and employing the dispatch order reception time toprepare actions required to control the each of the buildings tooptimally shed energy specified in a corresponding dispatch order. 16.The method as recited in claim 15, wherein the plurality of lag valuesindicates shifts of the energy consumption values to different time andoutside temperature values.
 17. The method as recited in claim 15,wherein the corresponding time values are less than or equal to theenergy lags.
 18. The method as recited in claim 15, wherein thecorresponding time values comprise hourly values and the plurality oflag values spans a 24-hour period.
 19. The method as recited in claim15, wherein the dispatch order reception time is predicted as a functionof demand response contract data and a specified threshold known totrigger the demand response program event.
 20. The method as recited inclaim 15, wherein the cumulative energy consumption for the buildingscomprises a future energy consumption timeline as a function of theoutside temperatures.
 21. The method as recited in claim 15, wherein theeach of the plurality of energy use data sets comprises a first portionof a corresponding each of a plurality of baseline energy use data sets,and wherein required energy consumption values resulting from shifts aretaken from a second portion of the corresponding each of a plurality ofbaseline energy use data sets.